Quantifying the dynamics of sub-daily to seasonal hydrological interactions of Ganges river with groundwater in a densely populated city: Implications to vulnerability of drinking water sources

A machine learning framework for spatio-temporal vulnerability mapping of groundwaters to nitrate in a data scarce region in Lenjanat Plain, Iran

The temporal aspect of groundwater vulnerability to contaminants such as nitrate is often overlooked, assuming vulnerability has a static nature. This study bridges this gap by employing machine learning with Detecting Breakpoints and Estimating Segments in Trend (DBEST) algorithm to reveal the underlying relationship between nitrate, water table, vegetation cover, and precipitation time series, that are related to agricultural activities and groundwater demand in a semi-arid region. The contamination probability of Lenjanat Plain has been mapped by comparing random forest (RF), support vector machine (SVM), and K-nearest-neighbors (KNN) models, fed with 32 input variables (dem-derived factors, physiography, distance and density maps, time series data). Also, imbalanced learning and feature selection techniques were investigated as supplementary methods, adding up to four scenarios. Results showed that the RF model, integrated with forward sequential feature selection (SFS) and SMOTE-Tomek resampling method, outperformed the other models (F1-score: 0.94, MCC: 0.83). The SFS techniques outperformed other feature selection methods in enhancing the accuracy of the models with the cost of computational expenses, and the cost-sensitive function proved more efficient in tackling imbalanced data issues than the other investigated methods. The DBEST method identified significant breakpoints within each time series dataset, revealing a clear association between agricultural practices along the Zayandehrood River and substantial nitrate contamination within the Lenjanat region. Additionally, the groundwater vulnerability maps created using the candid RF model and an ensemble of the best RF, SVM, and KNN models predicted mid to high levels of vulnerability in the central parts and the downhills in the southwest.

Declining groundwater and its impacts along Ganga riverfronts using combined Sentinel-1, GRACE, water levels, and rainfall data

The Indo-Gangetic Plains (IGP) in northern India are vast alluvial tracts with huge shallow aquifers, densely populated and agriculturally productive regions. In the last few decades, IGP has been facing water scarcity driven by erratic monsoon dynamics, anthropogenic activity, and hydroclimatic variability. In urban centers, continuous groundwater withdrawal leads to high stress, affecting surface deformation and a threat to buildings and infrastructures. An attempt has been made to explore the possible linkage and coupling between groundwater level, hydroclimatic variables, and subsidence in the Central Ganga Plains (CGP), in Varanasi metropolis using the combined multisensory multitemporal data, Sentinel-1 (2017-2023), GRACE (2003-2023), groundwater levels (1998-2023), and precipitation (2002-2023). Long-term hydrological response in the CGP shows continuous depletion (14.6 ± 5.6 mm/yr) in response to precipitation variability. Results show spatiotemporal variations between GWS, and precipitation estimate with nonlinear trend response due to associated inter-annual/inter-seasonal climate variability and anthropogenic water withdrawal, specifically during the observed drought years. The significant storage response in the urban center compared to a regional extent suggests the potential impact of exponentially increasing urbanization and building hydrological stress in the cities. The implications of reducing storage capacity show measured land subsidence (∼2-8 mm/yr) patterns developed along the meandering stretch of the Ganga riverfronts in Varanasi. The groundwater level data from the piezometric supports the hydroclimatic variables and subsidence coupling. Considering the vital link between water storage, food security, and socioeconomic growth, the results of this study require systematic inclusion in water management strategies as climate change seriously impacts water resources in the future.

Environmental tracers and groundwater residence time indicators reveal controls of arsenic accumulation rates beneath a rapidly developing urban area in Patna, India

Groundwater security is a pressing environmental and societal issue, particularly due to significantly increasing stressors on water resources, including rapid urbanization and climate change. Groundwater arsenic is a major water security and public health challenge impacting millions of people in the Gangetic Basin of India and elsewhere globally. In the rapidly developing city of Patna (Bihar) in northern India, we have studied the evolution of groundwater chemistry under the city following a three-dimensional sampling framework of multi-depth wells spanning the central urban zone in close proximity to the River Ganges (Ganga) and transition into peri-urban and rural areas outside city boundaries and further away from the river. Using inorganic geochemical tracers (including arsenic, iron, manganese, nitrate, nitrite, ammonium, sulfate, sulfide and others) and residence time indicators (CFCs and SF₆), we have evaluated the dominant hydrogeochemical processes occurring and spatial patterns in redox conditions across the study area. The distribution of arsenic and other redox-sensitive parameters is spatially heterogenous, and elevated arsenic in some locations is consistent with arsenic mobilization via reductive dissolution of iron hydroxides. Residence time indicators evidence modern (<~60-70 years) groundwater and suggest important vertical and lateral flow controls across the study area, including an apparent seasonal reversal in flow regimes near the urban center. An overall arsenic accumulation rate is estimated to be ~0.003 ± 0.003 μM.yr^-1 (equivalent to ~0.3 ± 0.2 μg.yr^-1), based on an average of CFC-11, CFC-12 and SF₆-derived models, with the highest rates of arsenic accumulation observed in shallow, near-river groundwaters also exhibiting elevated concentrations of nutrients including ammonium. Our findings have implications on groundwater management in Patna and other rapidly developing cities, including potential future increased groundwater vulnerability associated with surface-derived ingress from large-scale urban abstraction or in higher permeability zones of river-groundwater connectivity.

Tackling water security: A global need of cross-cutting approaches

The Virtual Special Issue entitled "Tackling Water Security" is mainly focused on water availability, water quality, management, governance, biotic or abiotic emerging contaminants and policy development in the Anthropocene. The issue is further dedicated to highlight the new opportunities and approaches to elevate the efficiency of water treatment and wastewater reuse. It has undergone an open call for papers and rigorous peer-review process, where each submission has been evaluated by the panel of experts. 43 articles have been selected from 85 submissions that represents the ongoing research and development activities. The message that emerged explicitly from nearly a hundred submissions to this special issue is that there is an urgent global need for cross-cutting approaches for the rational, quick, cost-effective and sustainable solutions for tackling water-security in the Anthropocene.

Influence of hydrogeochemical reactions along flow paths on contrasting groundwater arsenic and manganese distribution and dynamics across the Ganges River

The groundwater within the aquifers of the Ganges River delta exhibits significant spatial variability in concentrations of redox-sensitive solutes [e.g., arsenic (As), iron (Fe), manganese (Mn)]. The groundwater As and Mn concentrations show conspicuous contrasting distribution on the opposite banks of the Bhagirathi-Hooghly (B-H) River, the Indian distributary of the Ganges River. Here, we investigate the differences in hydrostratigraphic framework and groundwater evolutionary pathways across the B-H River that might have resulted in such variations. We developed a hydrostratigraphic model for the region and also used inverse reaction-path modeling along three hypothesized end-member flow paths to understand the dominant processes that might control As and Mn cycling within the aquifers. Our results indicate that the variability of As and Mn across the B-H River is a function of a complex interplay between the aquifer architecture, groundwater chemistry, and redox conditions.